Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better

@article{Zi2021RevisitingAR,
  title={Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better},
  author={Bojia Zi and Shihao Zhao and Xingjun Ma and Yu-Gang Jiang},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={16423-16432}
}
Adversarial training is one effective approach for training robust deep neural networks against adversarial attacks. While being able to bring reliable robustness, adversarial training (AT) methods in general favor high capacity models, i.e., the larger the model the better the robustness. This tends to limit their effectiveness on small models, which are more preferable in scenarios where storage or computing resources are very limited (e.g., mobile devices). In this paper, we leverage the… 

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